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2 | RecursiveTools FastJet contrib
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3 | ------------------------------------------------------------------------
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4 |
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5 | The RecursiveTools FastJet contrib aims to provide a common contrib
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6 | for a number of tools that involve recursive reclustering/declustering
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7 | of a jet for tagging or grooming purposes.
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8 |
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9 | Currently it contains:
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10 |
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11 | - ModifiedMassDropTagger
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12 | This corresponds to arXiv:1307.0007 by Mrinal Dasgupta, Alessandro
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13 | Fregoso, Simone Marzani and Gavin P. Salam
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14 |
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15 | - SoftDrop
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16 | This corresponds to arXiv:1402.2657 by Andrew J. Larkoski, Simone
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17 | Marzani, Gregory Soyez, Jesse Thaler
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18 |
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19 | - RecursiveSoftDrop
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20 | - BottomUpSoftDrop
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21 | This corresponds to arXiv:1804.03657 by Frederic Dreyer, Lina
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22 | Necib, Gregory Soyez and Jesse Thaler
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23 |
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24 | - IteratedSoftDrop
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25 | This corresponds to arXiv:1704.06266 by Christopher Frye, Andrew J.
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26 | Larkoski, Jesse Thaler, Kevin Zhou
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27 |
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28 | - Recluster
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29 | A generic tool to recluster a given jet into subjets
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30 | Note: a Recluster class is available natively in FastJet since v3.1.
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31 | Users are therefore encouraged to use the FastJet version
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32 | rather than this one which is mostly provided for
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33 | compatibility of this contrib with older versions of FastJet.
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34 |
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35 | The interface for these tools is described in more detail below, with
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36 | all of the available options documented in the header files.
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37 |
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38 | One note about nomenclature. A groomer is a procedure that takes a
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39 | PseudoJet and always returns another (non-zero) PseudoJet. A tagger is
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40 | a procedure that takes a PseudoJet, and either returns another PseudoJet
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41 | (i.e. tags it) or returns an empty PseudoJet (i.e. doesn't tag it).
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42 |
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43 | ------------------------------------------------------------------------
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44 | ModifiedMassDropTagger
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45 | ------------------------------------------------------------------------
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46 |
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47 | The Modified Mass Drop Tagger (mMDT) recursively declusters a jet,
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48 | following the largest pT subjet until a pair of subjets is found that
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49 | satisfy the symmetry condition on the energy sharing
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50 |
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51 | z > z_cut
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52 |
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53 | where z_cut is a predetermined value. By default, z is calculated as
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54 | the scalar pT fraction of the softest subjet. Note that larger values
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55 | of z_cut correspond to a more restrictive tagging criteria.
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56 |
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57 | By default, mMDT will first recluster the jet using the CA clustering
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58 | algorithm, which means that mMDT can be called on any jet, regardless
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59 | of the original jet finding measure.
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60 |
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61 | A default mMDT can be created via
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62 |
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63 | double z_cut = 0.10;
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64 | ModifiedMassDropTagger mMDT(z_cut);
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65 |
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66 | More options are available in the full constructor. To apply mMDT,
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67 | one simply calls it on the jet of interest.
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68 |
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69 | PseudoJet tagged_jet = mMDT(original_jet);
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70 |
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71 | Note that mMDT is a tagger, such that tagged_jet will only be non-zero
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72 | if the symmetry cut z > z_cut is satisfied by some branching of the
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73 | clustering tree.
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74 |
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75 | To gain additional information about the mMDT procedure, one can use
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76 |
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77 | tagged_jet.structure_of<ModifiedMassDropTagger>()
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78 |
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79 | which gives access to information about the delta_R between the tagged
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80 | subjets, their z value, etc.
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81 |
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82 | ------------------------------------------------------------------------
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83 | SoftDrop
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84 | ------------------------------------------------------------------------
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85 |
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86 | The SoftDrop procedure is very similar to mMDT, albeit with a
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87 | generalised symmetry condition:
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88 |
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89 | z > z_cut * (R / R0)^beta
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90 |
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91 | Note that larger z_cut and smaller beta correspond to more aggressive
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92 | grooming of the jet.
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93 |
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94 | SoftDrop is intended to be used as a groomer (instead of as a tagger),
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95 | such that if the symmetry condition fails throughout the whole
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96 | clustering tree, SoftDrop will still return a single particle in the
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97 | end. Apart from the tagger/groomer distinction, SoftDrop with beta=0 is
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98 | the same as mMDT.
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99 |
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100 | A default SoftDrop groomer can be created via:
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101 |
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102 | double z_cut = 0.10;
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103 | double beta = 2.0;
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104 | double R0 = 1.0; // this is the default value
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105 | SoftDrop sd(z_cut,beta,R0);
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106 |
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107 | and acts on a desired jet as
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108 |
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109 | PseudoJet groomed_jet = sd(original_jet);
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110 |
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111 | and additional information can be obtained via
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112 |
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113 | groomed_jet.structure_of<SoftDrop>()
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114 |
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115 | SoftDrop is typically called with beta > 0, though beta < 0 is still a
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116 | viable option. Because beta < 0 is infrared-collinear unsafe in
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117 | grooming mode, one probably wants to switch to tagging mode for negative
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118 | beta, via set_tagging_mode().
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119 |
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120 | ------------------------------------------------------------------------
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121 | RecursiveSoftDrop
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122 | ------------------------------------------------------------------------
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123 |
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124 | The RecursiveSoftDrop procedure applies the Soft Drop procedure N times
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125 | in a jet in order to find up to N+1 prongs. N=0 makes no modification
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126 | to the jet, and N=1 is equivalent to the original SoftDrop.
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127 |
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128 | Once one has more than one prong, one has to decide which will be
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129 | declustered next. At each step of the declustering procedure, one
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130 | undoes the clustering which has the largest declustering angle
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131 | (amongst all the branches that are searched for substructure). [see
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132 | "set_fixed_depth" below for an alternative]
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133 |
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134 | Compared to SoftDrop, RecursiveSoftDrop takes an extra argument N
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135 | specifying the number of times the SoftDrop procedure is recursively
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136 | applied. Negative N means that the procedure is applied until no
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137 | further substructure is found (i.e. corresponds to taking N=infinity).
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138 |
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139 | double z_cut = 0.10;
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140 | double beta = 2.0;
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141 | double R0 = 1.0; // this is the default value
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142 | int N = -1;
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143 | RecursiveSoftDrop rsd(z_cut, beta, N, R0);
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144 |
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145 | One then acts on a jet as
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146 |
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147 | PseudoJet groomed_jet = rsd(jet)
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148 |
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149 | and get additional information via
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150 |
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151 | groomed_jet.structure_of<RecursiveSoftDrop>()
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152 |
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153 | ------------------------------------------------------------------------
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154 | IteratedSoftDrop
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155 | ------------------------------------------------------------------------
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156 |
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157 | Iterated Soft Drop (ISD) is a repeated variant of SoftDrop. After
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158 | performing the Soft Drop procedure once, it logs the groomed symmetry
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159 | factor, then recursively performs Soft Drop again on the harder
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160 | branch. This procedure is repeated down to an (optional) angular cut
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161 | theta_cut, yielding a set of symmetry factors from which observables
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162 | can be built.
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163 |
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164 | An IteratedSoftDrop tool can be created as follows:
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165 |
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166 | double beta = -1.0;
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167 | double z_cut = 0.005;
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168 | double theta_cut = 0.0;
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169 | double R0 = 0.5; // characteristic radius of jet algorithm
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170 | IteratedSoftDrop isd(beta, z_cut, double theta_cut, R0);
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171 |
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172 | By default, ISD applied on a jet gives a result of type
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173 | IteratedSoftDropInfo that can then be probed to obtain physical
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174 | observables
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175 |
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176 | IteratedSoftDropInfo isd_info = isd(jet);
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177 |
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178 | unsigned int multiplicity = isd_info.multiplicity();
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179 | double kappa = 1.0; // changes angular scale of ISD angularity
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180 | double isd_width = isd_info.angularity(kappa);
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181 | vector<pair<double,double> > zg_thetags = isd_info.all_zg_thetag();
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182 | vector<pair<double,double> > zg_thetags = isd_info();
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183 | for (unsigned int i=0; i< isd_info.size(); ++i){
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184 | cout << "(zg, theta_g)_" << i << " = "
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185 | << isd_info[i].first << " " << isd_info[i].second << endl;
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186 | }
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187 |
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188 | Alternatively, one can directly get the multiplicity, angularity, and
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189 | (zg,thetag) pairs from the IteratedSoftDrop class, at the expense of
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190 | re-running the declustering procedure:
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191 |
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192 | unsigned int multiplicity = isd.multiplicity(jet);
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193 | double isd_width = isd.angularity(jet, 1.0);
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194 | vector<pair<double,double> > zg_thetags = isd.all_zg_thetag(jet);
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195 |
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196 |
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197 | Note: the iterative declustering procedure is the same as what one
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198 | would obtain with RecursiveSoftDrop with an (optional) angular cut
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199 | and recursing only in the hardest branch [see the "Changing
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200 | behaviour" section below for details], except that it returns some
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201 | information about the jet instead of a modified jet as RSD does.
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202 |
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203 |
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204 | ------------------------------------------------------------------------
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205 | BottomUpSoftDrop
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206 | ------------------------------------------------------------------------
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207 |
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208 | This is a bottom-up version of the RecursiveSoftDrop procedure, in a
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209 | similar way as Pruning can be seen as a bottom-up version of Trimming.
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210 |
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211 | In practice, the jet is reclustered and at each step of the clustering
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212 | one checks the SoftDrop condition
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213 |
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214 | z > z_cut * (R / R0)^beta
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215 |
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216 | If the condition is met, the pair is recombined. If the condition is
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217 | not met, only the hardest of the two objects is kept for further
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218 | clustering and the softest is rejected.
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219 |
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220 | ------------------------------------------------------------------------
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221 | Recluster
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222 | ------------------------------------------------------------------------
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223 |
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224 | *** NOTE: this is provided only for backwards compatibility ***
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225 | *** with FastJet <3.1. For FastJet >=3.1, the native ***
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226 | *** fastjet::Recluster is used instead ***
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227 |
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228 | The Recluster class allows the constituents of a jet to be reclustered
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229 | with a different recursive clustering algorithm. This is used
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230 | internally in the mMDT/SoftDrop/RecursiveSoftDrop/IteratedSoftDrop
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231 | code in order to recluster the jet using the CA algorithm. This is
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232 | achieved via
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233 |
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234 | Recluster ca_reclusterer(cambridge_algorithm,
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235 | JetDefinition::max_allowable_R);
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236 | PseudoJet reclustered_jet = ca_reclusterer(original_jet);
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237 |
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238 | Note that reclustered_jet creates a new ClusterSequence that knows to
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239 | delete_self_when_unused.
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240 |
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241 | ------------------------------------------------------------------------
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242 | Changing behaviour
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243 | ------------------------------------------------------------------------
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244 |
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245 | The behaviour of the all the tools provided here
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246 | (ModifiedMassDropTagger, SoftDrop, RecursiveSoftDrop and
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247 | IteratedSoftDrop) can be tweaked using the following options:
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248 |
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249 | SymmetryMeasure = {scalar_z, vector_z, y, theta_E, cos_theta_E}
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250 | [constructor argument]
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251 | : The definition of the energy sharing between subjets, with 0
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252 | corresponding to the most asymmetric.
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253 | . scalar_z = min(pt1,pt2)/(pt1+pt2) [default]
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254 | . vector_z = min(pt1,pt2)/pt_{1+2}
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255 | . y = min(pt1^2,pt2^2)/m_{12}^2 (original y from MDT)
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256 | . theta_E = min(E1,E2)/(E1+E2),
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257 | with angular measure theta_{12}^2
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258 | . cos_theta_E = min(E1,E2)/(E1+E2),
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259 | with angular measure 2[1-cos(theta_{12})]
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260 | The last two variants are meant for use in e+e- collisions,
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261 | together with the "larger_E" recursion choice (see below)
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262 |
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263 | RecursionChoice = {larger_pt, larger_mt, larger_m, larger_E}
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264 | [constructor argument]
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265 | : The path to recurse through the tree after the symmetry condition
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266 | fails. Options refer to transverse momentum (pt), transverse mass
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267 | (mt=sqrt(pt^2+m^2), mass (m) or energy (E). the latter is meant
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268 | for use in e+e- collisions
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269 |
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270 | mu_cut [constructor argument]
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271 | : An optional mass drop condition
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272 |
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273 | set_subtractor(subtractor*) [or subtractor as a constructor argument]
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274 | : provide a subtractor. When a subtractor is supplied, the
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275 | kinematic constraints are applied on subtracted 4-vectors. In
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276 | this case, the result of the ModifiedMassDropTagger/SoftDrop is a
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277 | subtracted PseudoJet, and it is assumed that
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278 | ModifiedMassDropTagger/SoftDrop is applied to an unsubtracted jet.
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279 | The latter default can be changed by calling
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280 | set_input_jet_is_subtracted().
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281 |
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282 | set_reclustering(bool, Recluster*)
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283 | : An optional setting to recluster a jet with a different jet
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284 | recursive jet algorithm. The code is only designed to give sensible
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285 | results with the CA algorithm, but other reclustering algorithm
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286 | (especially kT) may be appropriate in certain contexts.
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287 | Use at your own risk.
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288 |
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289 | set_grooming_mode()/set_tagging_mode()
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290 | : In grooming mode, the algorithm will return a single particle if the
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291 | symmetry condition fails for the whole tree. In tagging mode, the
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292 | algorithm will return an zero PseudoJet if no symmetry conditions
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293 | passes. Note that ModifiedMassDropTagger defaults to tagging mode
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294 | and SoftDrop defaults to grooming mode.
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295 |
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296 | set_verbose_structure(bool)
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297 | : when set to true, additional information will be stored in the jet
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298 | structure. This includes in particular values of symmetry,
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299 | delta_R, and mu of dropped branches
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300 |
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301 | For the specific case of RecursiveSoftDrop, additional tweaking is
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302 | possible via the following methods
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303 |
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304 | set_fixed_depth_mode(bool)
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305 | : when this is true, RSD will recurse (N times) into all the
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306 | branches found during the previous iteration [instead of recursing
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307 | through the largest declustering angle until N prongs have been
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308 | found]. This yields at most 2^N prong. For infinite N, the two
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309 | options are equivalent.
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310 |
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311 | set_dynamical_R0(bool)
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312 | : By default the angles in the SD condition are normalised to the
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313 | parameter R0. With "dynamical R0", RSD will dynamically adjust R0
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314 | to be the angle between the two prongs found during the previous
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315 | iteration.
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316 |
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317 | set_hardest_branch_only(bool)
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318 | : When substructure is found, only recurse into the hardest of the
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319 | two branches for further substructure search. This uses the class
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320 | RecursionChoice.
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321 |
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322 | set_min_deltaR_squared(double):
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323 | : set a minimal angle (squared) at which we stop the declustering
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324 | procedure. This cut is ineffective for negative values of the
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325 | argument.
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326 |
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327 | ------------------------------------------------------------------------
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328 | Technical Details
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329 | ------------------------------------------------------------------------
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330 |
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331 | Both ModifiedMassDropTagger and SoftDrop inherit from
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332 | RecursiveSymmetryCutBase, which provides a common codebase for recursive
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333 | declustering of a jet with a symmetry cut condition. A generic
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334 | RecursiveSymmetryCutBase depends on the following (virtual) functions
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335 | (see header file for exact full specs, including constness):
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336 |
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337 | double symmetry_cut_fn(PseudoJet &, PseudoJet &)
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338 | : The function that defines the symmetry cut. This is what actually
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339 | defines different recursive declustering schemes, and all classes
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340 | that inherit from RecursiveSymmetryCutBase must define this
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341 | function.
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342 |
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343 | string symmetry_cut_description()
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344 | : the string description of the symmetry cut.
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345 |
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346 | ------------------------------------------------------------------------
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